Optimization, defined as the process of selecting the best one among all of the possible solutions to a problem within certain constraints, is used for the solution of engineering and numerical functions. The metaheuristics algorithm used for the solution of a wide variety of problems has been developed by influencing the behavior of living things in nature. The Artificial Algae Algorithm (AAA), proposed by Uymaz and one of the biologically inspired optimizations, was used in this study. The AAA is one of the various methods of numerical optimization. In this study, a fine tuning of the control parameters for the AAA was implemented over its different kernel parameters. For this process, the AAA was tested on eight different benchmark functions with different characteristics by making finer adjustments over the parameters. The effect of the parameters was analyzed in terms of both solution quality and convergence speed.
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